We consider the estimation of local grey level image structure in terms of a lay ered representation This type of representation has recently been successfully used to segment various objects from clutter using either optical ow or stereo disparity infor mation We argue that the same type of representation is useful for grey level data in that it allows for the estimation of properties for each...

We explore the use of Optimal Mixture Models to represent topics. We analyze two broad classes of mixture models: set-based and weighted. We provide an original proof that estimation of set-based models is NP-hard, and therefore not feasible. We argue that weighted models are superior to set-based models, and the solution can be estimated by a simple gradient descent technique. We demonstrate t...

Alternative specifications of univariate asymmetric Laplace models are described and investigated. A more general mixture model is then introduced. Bivariate extensions of these models are discussed in some detail, with particular emphasis on associated parameter estimation strategies. Multivariate versions of the models are briefly introduced.